The 1−127 HA2 Construct of Influenza Virus Hemagglutinin Induces Cell−Cell Hemifusion
Bibliographic record
Abstract
Conformational changes in the HA2 subunit of influenza hemagglutinin (HA) are coupled to membrane fusion. We investigated the fusogenic activity of the polypeptide FHA2 representing 127 amino-terminal residues of the ectodomain of HA2. While the conformation of FHA2 both at neutral and at low pH is nearly identical to the final low-pH conformation of HA2, FHA2 still induces lipid mixing between liposomes in a low-pH-dependent manner. Here, we found that FHA2 induces lipid mixing between bound cells, indicating that the "spring-loaded" energy is not required for FHA2-mediated membrane merger. Although, unlike HA, FHA2 did not form an expanding fusion pore, both acidic pH and membrane concentrations of FHA2, required for lipid mixing, have been close to those required for HA-mediated fusion. Similar to what is observed for HA, FHA2-induced lipid mixing was reversibly blocked by lysophosphatidylcholine and low temperature, 4 degrees C. The same genetic modification of the fusion peptide inhibits both HA- and FHA2-fusogenic activities. The kink region of FHA2, critical for FHA2-mediated lipid mixing, was exposed in the low-pH conformation of the whole HA prior to fusion. The ability of FHA2 to mediate lipid mixing very similar to HA-mediated lipid mixing is consistent with the hypothesis that hemifusion requires just a portion of the energy released in the conformational change of HA at acidic pH.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".